This ‘digging’ into temperature data is getting kind of addictive. The previous post asked the simple question ” how many [temperature stations] have data adjusted?” and showed the proportions of adjusted data by WMO region. But, as soon as you ask the question – “Does adjustment affect temperature trends?” you immediately want to follow it up with “By how much?” and “In what way?”, then “Where on the globe?” So here is a taster.A quick recap first. Kevin’s TEKtemp database (to which access is available for anyone wishing to look at the data in database form – just post a comment or email Kevin – more info here) and a bit of bespoke code, has allowed us to look at and compare the temperature trends at individual temperature stations across the globe and to produce maps that show regions with cooling and warming trends. The figures below show examples of TEKtemp graphs for two of the stations in the NOAA GHCN dataset that have adjustments which affect the temperature trends. Note that Figure 1 (Orland, CA) has only minor adjustments that reduce the temperature trend slightly reducing the cooling trend; Figure 2 (Rock Springs Airport, WY) has major adjustments that turn a warming trend into a cooling trend.

Figure 1. Temperature plots and trends for Orland, CA

Figure 2. Temperature plots and trends for Rock Springs, WY

The piece of code Kevin has written to plot graphs and calculate trends has a minimum data QC level of 20 years; any year with one or more missing months is not included in the plot. Comparing the trends by station in a large table has been interesting to say the least. Of the datasets that qualified for trend calculation, 1065 were not adjusted by NOAA, whereas 4508 were adjusted in some way. Of the 4508, 2319 showed an increased trend and 1955 showed a decreased trend after adjustment; 234 had minor adjustments which had no effect on the trend.

I have initially simply calculated the difference between the trends. This gives me a number for each station record that refects both the magnitude and direction of adjustment. I’ve plotted that against the raw data trends (Figure 3) and separately against the adjusted data trends (Figure 4) so these are, in effect, ‘before’ and ‘after’ graphs.

Figure 3. Distribution of adjustments vs raw data trends.

Figure 4. Distribution of adjustments vs adjusted data trends.

These two figures made me go ‘wow’, but I’m not sure that I should be that surprised, after all we know that some data is warmed by adjustment and some is cooled by it. Figure 3 shows there is a lot of cooling data that will be warmed by adjustment and a lot of warm data that will be cooled by adjustment, overall there are more points to the right of the Y axis and therefore more warming than cooling station trends. Figure 4 suggests that, after adjustment there is even more data with warmig trend, but perhaps this is just a ‘by eye’ bias.

Figure 5. Distibution of data trends of raw data vs adjusted data.

Figure 5 on the other hand confirms this – definately more warming than cooling data and it SEEMS to be more warmed by adjustment. Could this really be?

[Update] I had hesitated to add Figure 6 when I first wrote this post as it is complex and I was not confident about how to present it, however I think it shows the adjustments rather well. To understand it, consider that there are six types of adjustments that affect the temperature trends:

Warming to cooling

Cooling to warming

Warming to less warming

Warming to increased warming

Cooling to less cooling

Cooling to increased cooling

The six parts of Figure 6 below are the exploded out ‘parts’ of the Figures 3 and 4, showing each of these types of adjustment. The comparison on each graph of the raw and adjusted slope gives an idea of just how much change there is to some of the individual temperature records.

I find this set of graphs quite persuasive. I do believe adjustment is necessary, and as perhaps anticipated, the vast majority of adjustements are small and make only a small difference to the overall trend, however there are some very large adjustments and some of the most extreme trends are adjusted to produce an extreme trend of the opposite sign. Kevin had previously explored reported many of these as physically unjustifiable. I concur. In Part 2 I’ll look more at the magnitude and distribution of these six types of adjustments.